I would recommend looking around on HuggingFace altough I found it a bit intimidating at the beginning. The place is just HUGE and they assume some knowledge.
I would also recommend creating a platform user on OpenAI and/or Anthropic and look up their docs. The accounts there are free but if you put a few dollars in there, you can actually make requests against their APIs which is the most simple way of playing around with LLMs imho.
Here are some topics you could do some research about:
- Foundation models (e.g., GPT, BERT, T5)
- Transformer architecture
- Natural Language Processing (NLP) basics
- Prompt engineering
- Fine-tuning and transfer learning
- Ethical considerations in AI
- AI safety and alignment
- Large Language Models (LLMs)
- Generative models for images (e.g., DALL-E, Stable Diffusion)
- AI frameworks and libraries (e.g., TensorFlow, PyTorch, Hugging Face)
- AI APIs and integration (also frameworks to build with AI like LangChain/ LangGraph)
- Vector databases and embeddings
- RAG
- Reinforcement Learning from Human Feedback (RLHF)
- AI model evaluation and benchmarking
- AI-assisted coding and code generation
1. Spend 6h going through this video: https://www.youtube.com/watch?v=1vkb7BCMQd0
2. Go through Google's intro to ML crash course and pre-reqs: https://developers.google.com/machine-learning/crash-course/...
3. Refer to other videos on Youtube (3B1B and StatQuest are couple of my favs) as you go through no 2 above
Spend about a month on the above and then see what you really want to dig into next. There could be a few different ways after the above, but one way is speedrunning through 1st sem coursework of any top ML grad school program
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Here is a suggested path to learn AI/ML fundamentals as a software engineer:
Start with the basics of machine learning:
Learn the core concepts of supervised, unsupervised, and reinforcement learning.
Understand the key algorithms like linear regression, logistic regression, decision trees, random forests, etc.
Get familiar with common evaluation metrics like accuracy, precision, recall, F1-score.
Dive into deep learning:
Learn the fundamentals of neural networks, including architectures like feedforward, convolutional, and recurrent neural networks.
Understand the training process, loss functions, activation functions, and optimization techniques.
Explore popular deep learning frameworks like TensorFlow, PyTorch, or Keras.
Learn about data preprocessing and feature engineering:
Understand how to handle missing data, outliers, and noisy data.
Explore techniques for feature selection, dimensionality reduction, and data normalization.
Practice implementing ML/DL models:
Work through tutorials and build simple projects using the concepts you've learned.
Try applying ML/DL to real-world datasets and problems in your domain.
Participate in online coding challenges and competitions like Kaggle.
Expand your knowledge:
Learn about advanced topics like transfer learning, generative models, and reinforcement learning.
Stay up-to-date with the latest research and trends in the AI/ML field.
Consider taking online courses or pursuing a relevant certification.
Develop practical skills:
Learn how to deploy and monitor ML/DL models in production.
Understand the challenges of scaling and optimizing AI systems.
Familiarize yourself with tools for data visualization, model interpretation, and MLOps.
[0] https://kagi.com/fastgpt?cf-turnstile-response=&query=%09Wha....